Journal
BIOINFORMATICS
Volume 38, Issue 18, Pages 4440-4441Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac521
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Funding
- Fondazione AIRC per la Ricerca sul Cancro (AIRC) [IG 2019 ID 23825]
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Mathematical models are effective in studying cancer development, and Phase Field Models (PFMs) can accurately simulate cancer growth and related phenomena. However, the implementation of such models is rarely published, limiting access to these techniques. To bridge this gap, we have developed an open-source Python package called Mocafe, which implements some important PFMs reported in the literature and is designed to be extensible.
Mathematical models are effective in studying cancer development at different scales from metabolism to tissue. Phase Field Models (PFMs) have been shown to reproduce accurately cancer growth and other related phenomena, including expression of relevant molecules, extracellular matrix remodeling and angiogenesis. However, implementations of such models are rarely published, reducing access to these techniques. To reduce this gap, we developed Mocafe, a modular open-source Python package that implements some of the most important PFMs reported in the literature. Mocafe is designed to handle both PFMs purely based on differential equations and hybrid agent-based PFMs. Moreover, Mocafe is meant to be extensible, allowing the inclusion of new models in future releases.
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